{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:19.307784Z",
     "start_time": "2019-11-25T01:39:19.293589Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:19.518741Z",
     "start_time": "2019-11-25T01:39:19.511844Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:19.938871Z",
     "start_time": "2019-11-25T01:39:19.841748Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"cardio_train.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:20.032852Z",
     "start_time": "2019-11-25T01:39:20.015409Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18393</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20228</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>18857</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>17474</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0  18393       2     168    62.0    110     80            1     1      0   \n",
       "1   1  20228       1     156    85.0    140     90            3     1      0   \n",
       "2   2  18857       1     165    64.0    130     70            3     1      0   \n",
       "3   3  17623       2     169    82.0    150    100            1     1      0   \n",
       "4   4  17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:20.225212Z",
     "start_time": "2019-11-25T01:39:20.193826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 70000 entries, 0 to 69999\n",
      "Data columns (total 13 columns):\n",
      "id             70000 non-null int64\n",
      "age            70000 non-null int64\n",
      "gender         70000 non-null int64\n",
      "height         70000 non-null int64\n",
      "weight         70000 non-null float64\n",
      "ap_hi          70000 non-null int64\n",
      "ap_lo          70000 non-null int64\n",
      "cholesterol    70000 non-null int64\n",
      "gluc           70000 non-null int64\n",
      "smoke          70000 non-null int64\n",
      "alco           70000 non-null int64\n",
      "active         70000 non-null int64\n",
      "cardio         70000 non-null int64\n",
      "dtypes: float64(1), int64(12)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "#无缺失\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:20.497225Z",
     "start_time": "2019-11-25T01:39:20.374346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49972.419900</td>\n",
       "      <td>19468.865814</td>\n",
       "      <td>1.349571</td>\n",
       "      <td>164.359229</td>\n",
       "      <td>74.205690</td>\n",
       "      <td>128.817286</td>\n",
       "      <td>96.630414</td>\n",
       "      <td>1.366871</td>\n",
       "      <td>1.226457</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.053771</td>\n",
       "      <td>0.803729</td>\n",
       "      <td>0.499700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28851.302323</td>\n",
       "      <td>2467.251667</td>\n",
       "      <td>0.476838</td>\n",
       "      <td>8.210126</td>\n",
       "      <td>14.395757</td>\n",
       "      <td>154.011419</td>\n",
       "      <td>188.472530</td>\n",
       "      <td>0.680250</td>\n",
       "      <td>0.572270</td>\n",
       "      <td>0.283484</td>\n",
       "      <td>0.225568</td>\n",
       "      <td>0.397179</td>\n",
       "      <td>0.500003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10798.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>-70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25006.750000</td>\n",
       "      <td>17664.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50001.500000</td>\n",
       "      <td>19703.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74889.250000</td>\n",
       "      <td>21327.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99999.000000</td>\n",
       "      <td>23713.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>16020.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id           age        gender        height        weight  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean   49972.419900  19468.865814      1.349571    164.359229     74.205690   \n",
       "std    28851.302323   2467.251667      0.476838      8.210126     14.395757   \n",
       "min        0.000000  10798.000000      1.000000     55.000000     10.000000   \n",
       "25%    25006.750000  17664.000000      1.000000    159.000000     65.000000   \n",
       "50%    50001.500000  19703.000000      1.000000    165.000000     72.000000   \n",
       "75%    74889.250000  21327.000000      2.000000    170.000000     82.000000   \n",
       "max    99999.000000  23713.000000      2.000000    250.000000    200.000000   \n",
       "\n",
       "              ap_hi         ap_lo   cholesterol          gluc         smoke  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean     128.817286     96.630414      1.366871      1.226457      0.088129   \n",
       "std      154.011419    188.472530      0.680250      0.572270      0.283484   \n",
       "min     -150.000000    -70.000000      1.000000      1.000000      0.000000   \n",
       "25%      120.000000     80.000000      1.000000      1.000000      0.000000   \n",
       "50%      120.000000     80.000000      1.000000      1.000000      0.000000   \n",
       "75%      140.000000     90.000000      2.000000      1.000000      0.000000   \n",
       "max    16020.000000  11000.000000      3.000000      3.000000      1.000000   \n",
       "\n",
       "               alco        active        cardio  \n",
       "count  70000.000000  70000.000000  70000.000000  \n",
       "mean       0.053771      0.803729      0.499700  \n",
       "std        0.225568      0.397179      0.500003  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      1.000000      0.000000  \n",
       "50%        0.000000      1.000000      0.000000  \n",
       "75%        0.000000      1.000000      1.000000  \n",
       "max        1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:21.331120Z",
     "start_time": "2019-11-25T01:39:20.564188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 11, 8\n",
    "df['years'] = (df['age'] / 365).round().astype('int')\n",
    "sns.countplot(x='years', hue='cardio', data = df, palette=['lightgreen', 'dodgerblue']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以观察到55岁以上的人更容易得心血管疾病的。 从上面的表格中，我们可以看到ap_hi, ap_lo, weight 和height中有异常值。我们以后再处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:22.117876Z",
     "start_time": "2019-11-25T01:39:21.525425Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#让我们看看数据集中的分类变量及其分布：\n",
    "df_categorical = df.loc[:,['cholesterol','gluc', 'smoke', 'alco', 'active']]\n",
    "sns.countplot(x=\"variable\", hue=\"value\",data= pd.melt(df_categorical));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:39:23.347310Z",
     "start_time": "2019-11-25T01:39:22.358573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f8275633e80>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 635.275x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_long = pd.melt(df, id_vars=['cardio'], value_vars=['cholesterol','gluc', 'smoke', 'alco', 'active'])\n",
    "sns.factorplot(x=\"variable\", hue=\"value\", col=\"cardio\",\n",
    "                data=df_long, kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以清楚地看到，CVD患者的胆固醇和血糖水平较高。而且一般来说不太活跃，运动少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:32.264145Z",
     "start_time": "2019-11-25T01:40:32.221525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "1    161.355612\n",
       "2    169.947895\n",
       "Name: height, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#为了计算“1”在性别栏中代表女性还是男性，让我们计算每个性别的身高平均值。我们假设男人平均比女人高。\n",
    "df.groupby('gender')['height'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:32.444225Z",
     "start_time": "2019-11-25T01:40:32.429884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    45530\n",
       "2    24470\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:32.648012Z",
     "start_time": "2019-11-25T01:40:32.633680Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "1    1161\n",
       "2    2603\n",
       "Name: alco, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gender')['alco'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:33.278775Z",
     "start_time": "2019-11-25T01:40:33.267933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.5003\n",
       "1    0.4997\n",
       "Name: cardio, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cardio'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:34.200918Z",
     "start_time": "2019-11-25T01:40:34.073879Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardio</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.327343</td>\n",
       "      <td>0.172957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.323086</td>\n",
       "      <td>0.176614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender         1         2\n",
       "cardio                    \n",
       "0       0.327343  0.172957\n",
       "1       0.323086  0.176614"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['cardio'],df['gender'],normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:34.882695Z",
     "start_time": "2019-11-25T01:40:34.801636Z"
    }
   },
   "outputs": [],
   "source": [
    "#清理数据\n",
    "df.drop(df[(df['height'] > df['height'].quantile(0.975)) | (df['height'] < df['height'].quantile(0.025))].index,inplace=True)\n",
    "df.drop(df[(df['weight'] > df['weight'].quantile(0.975)) | (df['weight'] < df['weight'].quantile(0.025))].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:36.337939Z",
     "start_time": "2019-11-25T01:40:36.329597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diastilic pressure is higher than systolic one in 1082 cases\n"
     ]
    }
   ],
   "source": [
    "#此外，在某些情况下，舒张压高于收缩压，这也是不正确的。\n",
    "#去血压异常\n",
    "print(\"Diastilic pressure is higher than systolic one in {0} cases\".format(df[df['ap_lo']> df['ap_hi']].shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:42.478331Z",
     "start_time": "2019-11-25T01:40:42.421434Z"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(df[(df['ap_hi'] > df['ap_hi'].quantile(0.975)) | (df['ap_hi'] < df['ap_hi'].quantile(0.025))].index,inplace=True)\n",
    "df.drop(df[(df['ap_lo'] > df['ap_lo'].quantile(0.975)) | (df['ap_lo'] < df['ap_lo'].quantile(0.025))].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:43.489725Z",
     "start_time": "2019-11-25T01:40:43.122277Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diastilic pressure is higher than systolic one in 0 cases\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blood_pressure = df.loc[:,['ap_lo','ap_hi']]\n",
    "sns.boxplot(x = 'variable',y = 'value',data = blood_pressure.melt())\n",
    "print(\"Diastilic pressure is higher than systolic one in {0} cases\".format(df[df['ap_lo']> df['ap_hi']].shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:45.697996Z",
     "start_time": "2019-11-25T01:40:44.632417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "# 设置matplotlib图\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "# 画出热图，并校正长宽比\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,annot = True,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5});\n",
    "#我们可以看到年龄和胆固醇有显著的影响，但与目标阶层的相关性不是很高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:46.561766Z",
     "start_time": "2019-11-25T01:40:45.701520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "df_melt = pd.melt(frame=df, value_vars=['height'], id_vars=['gender'])\n",
    "plt.figure(figsize=(12, 10))\n",
    "ax = sns.violinplot(\n",
    "    x='variable', \n",
    "    y='value', \n",
    "    hue='gender', \n",
    "    split=True, \n",
    "    data=df_melt, \n",
    "    scale='count',\n",
    "    scale_hue=False,\n",
    "    palette=['lightgreen', 'dodgerblue']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:46.693713Z",
     "start_time": "2019-11-25T01:40:46.565118Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'catplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-95233713b534>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#计算身体质量指数(BMI):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'BMI'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weight'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'height'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"BMI\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"alco\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cardio\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"yellow\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"box\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'catplot'"
     ]
    }
   ],
   "source": [
    "#计算身体质量指数(BMI):\n",
    "df['BMI'] = df['weight']/((df['height']/100)**2)\n",
    "sns.catplot(x=\"gender\", y=\"BMI\", hue=\"alco\", col=\"cardio\", data=df, color = \"yellow\",kind=\"box\", height=10, aspect=.7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:47.610776Z",
     "start_time": "2019-11-25T01:40:47.601848Z"
    }
   },
   "outputs": [],
   "source": [
    "def feat_select(threshold):\n",
    "    abs_cor = correlations.abs()\n",
    "    features = abs_cor[abs_cor > threshold].index.tolist()\n",
    "    return features\n",
    "def model(mod,X_tr,X_te):\n",
    "    mod.fit(X_tr,y_train)\n",
    "    pred = mod.predict(X_te)\n",
    "    print('Model score = ',mod.score(X_te,y_test)*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:50.525335Z",
     "start_time": "2019-11-25T01:40:50.465240Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id             0.004723\n",
      "age            0.237391\n",
      "gender        -0.004828\n",
      "height        -0.030766\n",
      "weight         0.158004\n",
      "ap_hi          0.428547\n",
      "ap_lo          0.321554\n",
      "cholesterol    0.216638\n",
      "gluc           0.085880\n",
      "smoke         -0.023463\n",
      "alco          -0.014739\n",
      "active        -0.038563\n",
      "years          0.236911\n",
      "BMI            0.171964\n",
      "Name: cardio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# correlations with target class\n",
    "correlations = df.corr()['cardio'].drop('cardio')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:51.040096Z",
     "start_time": "2019-11-25T01:40:50.977818Z"
    }
   },
   "outputs": [],
   "source": [
    "# 切分数据集\n",
    "msk = np.random.rand(len(df))<0.85\n",
    "df_train_test = df[msk]\n",
    "df_val = df[~msk]\n",
    "\n",
    "X = df_train_test.drop('cardio',axis=1)\n",
    "y = df_train_test['cardio']\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2,random_state=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:40:56.437812Z",
     "start_time": "2019-11-25T01:40:52.288776Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold is 0.001\n",
      "Model score =  71.61397345823576 %\n",
      "Threshold is 0.002\n",
      "Model score =  71.61397345823576 %\n",
      "Threshold is 0.005\n",
      "Model score =  71.76034348165496 %\n",
      "Threshold is 0.01\n",
      "Model score =  71.76034348165496 %\n",
      "Threshold is 0.05\n",
      "Model score =  71.61397345823576 %\n",
      "Threshold is 0.1\n",
      "Model score =  71.56518345042934 %\n"
     ]
    }
   ],
   "source": [
    "# 逻辑回归\n",
    "lr = LogisticRegression()\n",
    "threshold = [0.001,0.002,0.005,0.01,0.05,0.1]\n",
    "for i in threshold:\n",
    "    print(\"Threshold is {}\".format(i))\n",
    "    feature_i = feat_select(i)\n",
    "    X_train_i = X_train[feature_i]\n",
    "    X_test_i = X_test[feature_i]\n",
    "    model(lr,X_train_i,X_test_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:46:01.240133Z",
     "start_time": "2019-11-25T01:46:01.225708Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',\n",
       "       'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'cardio', 'years',\n",
       "       'BMI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:49:09.342618Z",
     "start_time": "2019-11-25T01:49:09.327502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',\n",
       "       'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'years', 'BMI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:49:14.038322Z",
     "start_time": "2019-11-25T01:49:13.988891Z"
    }
   },
   "outputs": [],
   "source": [
    "#标准化\n",
    "scale = StandardScaler()\n",
    "scale.fit(X_train)\n",
    "X_train_scaled = scale.transform(X_train)\n",
    "X_train_ = pd.DataFrame(X_train_scaled, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:49:17.779442Z",
     "start_time": "2019-11-25T01:49:17.755376Z"
    }
   },
   "outputs": [],
   "source": [
    "scale.fit(X_test)\n",
    "X_test_scaled = scale.transform(X_test)\n",
    "X_test_ = pd.DataFrame(X_test_scaled, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:53:59.805776Z",
     "start_time": "2019-11-25T01:49:19.404936Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-63-ba3db251f859>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mknn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_k\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mpred_j\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_k\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mpred_j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/classification.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m         \u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0m_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    452\u001b[0m                 delayed_query(\n\u001b[1;32m    453\u001b[0m                     self._tree, X[s], n_neighbors, return_distance)\n\u001b[0;32m--> 454\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_even_slices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m             )\n\u001b[1;32m    456\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    777\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    330\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/base.py\u001b[0m in \u001b[0;36m_tree_query_parallel_helper\u001b[0;34m(tree, data, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    289\u001b[0m     \u001b[0munder\u001b[0m \u001b[0mPyPy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m     \"\"\"\n\u001b[0;32m--> 291\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_neighbors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimum k with optimum threshold\n",
    "for i in threshold:\n",
    "    feature = feat_select(i)\n",
    "    X_train_k = X_train_[feature]\n",
    "    X_test_k = X_test_[feature]\n",
    "    err = []\n",
    "    for j in range(1,30):\n",
    "        knn = KNeighborsClassifier(n_neighbors=j)\n",
    "        knn.fit(X_train_k,y_train)\n",
    "        pred_j = knn.predict(X_test_k)\n",
    "        err.append(np.mean(y_test != pred_j))\n",
    "\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.plot(range(1,30),err)\n",
    "    plt.title('Threshold of {}'.format(i))\n",
    "    plt.xlabel('K value')\n",
    "    plt.ylabel('Error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:53:59.811161Z",
     "start_time": "2019-11-25T01:51:06.113Z"
    }
   },
   "outputs": [],
   "source": [
    "# final feature selection with threshold 0.05\n",
    "feat_final = feat_select(0.05)\n",
    "print(feat_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:53:59.813443Z",
     "start_time": "2019-11-25T01:51:06.535Z"
    }
   },
   "outputs": [],
   "source": [
    "#knn\n",
    "X_train = X_train_[feat_final]\n",
    "X_val = np.asanyarray(df_val[feat_final])\n",
    "y_val = np.asanyarray(df_val['cardio'])\n",
    "\n",
    "scale.fit(X_val)\n",
    "X_val_scaled = scale.transform(X_val)\n",
    "X_val_ = pd.DataFrame(X_val_scaled,columns=df_val[feat_final].columns)\n",
    "# knn with k=15\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_val_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:53:59.816159Z",
     "start_time": "2019-11-25T01:51:07.023Z"
    }
   },
   "outputs": [],
   "source": [
    "#混淆矩阵热图\n",
    "cm=confusion_matrix(y_val,pred)\n",
    "cm_matrix = pd.DataFrame(cm, columns=['true', 'false'], index=['postive', 'negative'])\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')\n",
    "\n",
    "print('\\n',classification_report(y_val,pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T01:53:59.823752Z",
     "start_time": "2019-11-25T01:51:07.541Z"
    }
   },
   "outputs": [],
   "source": [
    "# Logistic regression\n",
    "lr.fit(X_train,y_train)\n",
    "pred = lr.predict(X_val_)\n",
    "\n",
    "#混淆矩阵热图\n",
    "cm=confusion_matrix(y_val,pred)\n",
    "cm_matrix = pd.DataFrame(cm, columns=['true', 'false'], index=['postive', 'negative'])\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')\n",
    "\n",
    "print('\\n',classification_report(y_val,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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